团队合作
集合(抽象数据类型)
主题(计算)
社会学
服务(商务)
任务(项目管理)
工程伦理学
心理学
实证研究
协同生产
知识管理
管理科学
计算机科学
工作(物理)
系统回顾
数据科学
空格(标点符号)
跟踪(心理语言学)
大数据
社会工作
领域(数学)
立场文件
多样性(控制论)
作者
Werner H. Kunz,László Sajtos,Carlos Flavián
出处
期刊:Journal of Service Management
[Emerald Publishing Limited]
日期:2025-09-17
卷期号:36 (4): 477-494
被引量:6
标识
DOI:10.1108/josm-04-2025-0194
摘要
Purpose The purpose is to advance the understanding of human-machine (H-M) collaboration in service industries, conceptualizing a framework that structures the research space and proposing a research agenda to guide future studies on optimizing collaboration dynamics, outcomes and ethical governance. Design/methodology/approach The authors use an artificial intelligence (AI)-based systematic literature based on the SERVSIG Literature Alert database to identify articles related to H-M collaboration. Insights from these papers were analyzed to (1) trace the evolution of H-M collaboration research, (2) formulate an integrative framework spanning foundational resources through outcomes and (3) develop a future research agenda. Findings The paper develops an integrative framework describing the foundation, process, and outcomes of H-M teamwork in service settings. It also introduces a set of new articles from the special issue. Social implications The paper underscores the ethical considerations (e.g. data biases, privacy and transparency) and broader societal concerns (e.g. job displacement and social inequality) and uniquely positions ethics as a cross-cutting theme (product, consumer and societal levels), moving beyond siloed ethical discussions in earlier work. Originality/value Unlike earlier work focusing on either human replacement or narrow task automation, this paper proposes a teamwork perspective, showing how AI and humans are working together and combining capabilities to achieve outcomes together. It maps diverse empirical studies to a comprehensive framework, demonstrating its applicability and enriching theoretical rigor with real-world evidence.
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